<img alt="selannel" width="350" src="https://github.com/feichtip/selanneal/blob/main/docs/_images/selanneal.png?raw=true"/>
Optimising selections with simulated annealing
==============================================
Selanneal is a simple package for optimising multivariate selections via a figure of merit.
The optimisation is performed for all given features simultaneously by utilising the simulated annealing method.
It relies on [numba](http://numba.pydata.org/) for just-in-time compilation of the algorithm.
The procedure works on binned data, so an n-dimensional histogram needs to be provided.
Currently, two modes of operation exist:
* **edges**: cut only the edges of each feature (results in "rectangular cuts")
* **bins**: select individual bins from a grid (for now limited to 2 feature dimensions)
This package was written for applications in high energy physics but can apply to general problems in statistical data analysis.
## usage
* install with
```console
python3 -m pip install selanneal
```
* tutorial notebooks for basic usage in *examples*
* training data is to be provided as numpy arrays representing the histogrammed number of signal and background events
* hyper-parameters to tune the optimisation: Tmin, Tmax, steps
* the default figure of merit is N_sig/sqrt(N_sig + N_bkg), but any objective function can be used
* additionally, a ROC curve can be obtained by maximising the purity for fixed signal efficiencies
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"description": "<img alt=\"selannel\" width=\"350\" src=\"https://github.com/feichtip/selanneal/blob/main/docs/_images/selanneal.png?raw=true\"/>\n\nOptimising selections with simulated annealing\n==============================================\n\nSelanneal is a simple package for optimising multivariate selections via a figure of merit.\nThe optimisation is performed for all given features simultaneously by utilising the simulated annealing method.\nIt relies on [numba](http://numba.pydata.org/) for just-in-time compilation of the algorithm.\nThe procedure works on binned data, so an n-dimensional histogram needs to be provided.\n\nCurrently, two modes of operation exist:\n* **edges**: cut only the edges of each feature (results in \"rectangular cuts\")\n* **bins**: select individual bins from a grid (for now limited to 2 feature dimensions)\n\nThis package was written for applications in high energy physics but can apply to general problems in statistical data analysis.\n\n## usage\n\n* install with\n```console\npython3 -m pip install selanneal\n```\n* tutorial notebooks for basic usage in *examples*\n* training data is to be provided as numpy arrays representing the histogrammed number of signal and background events\n* hyper-parameters to tune the optimisation: Tmin, Tmax, steps\n* the default figure of merit is N_sig/sqrt(N_sig + N_bkg), but any objective function can be used\n* additionally, a ROC curve can be obtained by maximising the purity for fixed signal efficiencies\n",
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